London Medical Imaging & Artificial Intelligence Centre for Value-Based Healthcare. Professor Reza Razavi Centre Director
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1 London Medical Imaging & Artificial Intelligence Centre for Value-Based Healthcare Professor Reza Razavi Centre Director
2 The traditional healthcare business model is changing Fee-for-service Fee-for-outcome Oncology Patient group experts Cardiology Primary care Patient Surgery population 1 Neurology Patient Emergency care population 2 Intensive care Patient Radiology population 3 Anesthesiology Patient Lab and pathology population 4 Other care professions Functional experts and teams Other providers Other hospitals Other rehab, elder care etc. Optimisation criteria No. of patients Health No. outcomes of = procedures and costs Reimbursement per patient Fee Healthcare costs Outpatient clinics Clinical demand definition of patient population Inpatient clinics Information provider, Decision enabler Unrestricted Siemens Healthcare GmbH, 2018
3 Our Centre Vision: Driving the Transformation towards Value-Based Healthcare Personalised diagnosis & therapy Big data support Creating Big data Benefits support for Artificial intelligence (AI) Artificial Patients: for intelligence Cost personalized efficient (AI) precision decisions: medicine for NHS: personalized Cost savings decisions: for the NHS UK: Transformative economic growth 50% By Enabling Time efficient automated reporting 50% Optimal treatment stratification Major redesign of clinical pathways to improve outcomes potential and reduction efficiency New business potential opportunities in medical reduction for UK companies in medical treatment and treatment job creation costs 1) costs 1) HC DI 1) Source: Page 3 Unrestricted Siemens Healthcare GmbH, 2017
4 Centre Overview Focus on 12 clinical pathways for maximum impact 10 SMEs core partners supported on site Siemens European Stratified Medicine R&D headquarters Co-location of academic, clinical and industry/sme staff: sandpit for rapid prototype iteration World-leading expertise in imaging, AI, clinical research Substantial computational infrastructure, in partnership with IBM & NVIDIA Bringing the algorithms to the data Engagement with NHS commissioners and health economics
5 Centre Members Thames Mammography AInostics
6 St Thomas Hospital Hub Radiologists/clinicians & health economics Researchers in advanced imaging & AI Wellcome/EPSRC Centre for Medical Engineering Multinational Industry (e.g. Siemens) Small & Medium Enterprises
7 Industrial strategy: growing SMEs Regulatory, QMS & data governance BRCs LHCRE Venture Capital Clinicians NHS commissioner engagement Business seminars KiTEC Wellcome/EPSRC Centre NHS data SMEs Health economics Computing infrastructure NIHR Med Tech Co-operative Siemens, GSK, NVIDIA, IBM HDR UK AI/data science experts Software development The Foundry IP & commercial strategy Medical imaging
8 Acute Chest Pain Key Question: Does introducing a CT scan for low to intermediate risk ACP patients result in earlier diagnosis and subsequent appropriate treatment or discharge? Interesting Facts/Benefits: At St Thomas 12 patients per day are admitted for observation with acute chest pain, at a cost of 1000 per patient. Cardiac CT costs % of patients could potentially be discharged earlier with this facility available. ACP patients are often discharged to GPs Patient Presents at A&E with Stable Chest Pain Clinical Assessment Admission pending further investigation with no definite diagnosis and are, subsequently, often referred back to Negative for CAD cardiology. NICE acknowledges the use of cardiac CT Patient Presents at A&E with Stable Chest Pain Clinical Assessment Non-invasive CT Coronary Angiography Discharge back to Primary care Rapid turnaround of scan and report CAD explaining symptoms within 4 hr wait Admission pending further investigation
9 Deep learning for automatic reporting of X-rays Fully-automated Initial application: tagging fully-automated of a raw image reading done of in chest milliseconds x-ray images One million Abnormal exams chest from radiographs Guy & were St Thomas detected Archives with a ( ) precision of 0.94; sensitivity of 0.95 and specificity of 0.71 Clinical Finding Sensitivity Specificity Accuracy Enlarged heart Opacity Medical Device Pleural Effusion Opacity Pleural Effusion Semantic segmentation learned from weak labels only Enlarged Heart Atelectasis/Collapse Pneumothorax Nodule Annotation COPD of all images required for training & testing Automated Edema tagging through deep nets for natural language modelling Enlarged heart Enlarged Heart Medical Device Infection Pesce E, et al. Radiology 2018 (in print)
10 Machine Learning for Organ Recognition in Fetal screening Run-time performance: 15 fps Baumgartner CF et al IEEE Trans Med Imaging Nov;36(11):
11 Deep Learning for Prognostic Modelling in Cancer For Recent the radiomics treatment studies of oesophageal have suggested cancer, neoadjuvant that texture features chemotherapy extracted (NC) from 18F-FDG is given as PET a first scans step can before predict surgery NC response Responders Deep neural to networks NC have offer improved an alternative survival radiomics after surgery, strategy: but prognosis they learn is predictive worse for imaging non responders features from raw scans Responder Non Responder Survival rates in 107 patients Best classifier using >100 texture features achieves 66.8% accuracy A convolutional network fusing adjacent slices reaches 73.4% Ypsilantis et al PLOS One
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